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Abstract：
UNASSIGNED: Machine learning is an important branch and supporting technology of artificial intelligence, we established four machine learning model for the drug sensitivity of Klebsiella pneumoniae to imipenem based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and compared their diagnostic effect.
UNASSIGNED: The data of MALDI-TOF-MS and imipenem sensitivity of 174 cases of K. pneumoniae isolated from clinical specimens in the laboratory of microbiology department of Tianjin Haihe Hospital from 2019 January to 2020 December were collected. The mass spectrometry and imipenem sensitivity of 70 cases of imipenem-sensitive and 70 resistant cases were randomly selected to establish the training set model, 17 cases of sensitive and 17 cases of resistant cases were randomly selected to establish the test set model. Mass spectral peak data were subjected to orthogonal partial least squares discriminant analysis (OPLS-DA), the training set data model was established by machine learning least absolute shrinkage and selection operator (LASSO) algorithm, logistic regression (LR) algorithm, support vector machines (SVM) algorithm, neural network (NN) algorithm, the area under the curve (AUC) and confusion matrix of training set and test set model were calculated and selected by Grid search and 3-fold Cross-validation respectively, the accuracy of the prediction model was verified by test set confusion matrix.
UNASSIGNED: The R²Y and Q² of OPLS-DA were 0.546 and 0.0178. The AUC of the best training set and test set models were 0.9726 and 0.9100, 1.0000 and 0.8581, 0.8462 and 0.6263, 1.0000 and 0.7180 evaluated by LASSO, LR, SVM and NN model respectively. The accuracy of the LASSO, LR, SVM and NN model were 87%, 79%, 62%, and 68% in test set, respectively.
UNASSIGNED: The LASSO prediction model of K. pneumoniae sensitivity to imipenem established in this study has a high accuracy rate and has potential clinical decision support ability.
UNASSIGNED: The data of MALDI-TOF-MS and imipenem sensitivity of 174 cases of K. pneumoniae isolated from clinical specimens in the laboratory of microbiology department of Tianjin Haihe Hospital from 2019 January to 2020 December were collected. The mass spectrometry and imipenem sensitivity of 70 cases of imipenem-sensitive and 70 resistant cases were randomly selected to establish the training set model, 17 cases of sensitive and 17 cases of resistant cases were randomly selected to establish the test set model. Mass spectral peak data were subjected to orthogonal partial least squares discriminant analysis (OPLS-DA), the training set data model was established by machine learning least absolute shrinkage and selection operator (LASSO) algorithm, logistic regression (LR) algorithm, support vector machines (SVM) algorithm, neural network (NN) algorithm, the area under the curve (AUC) and confusion matrix of training set and test set model were calculated and selected by Grid search and 3-fold Cross-validation respectively, the accuracy of the prediction model was verified by test set confusion matrix.
UNASSIGNED: The R²Y and Q² of OPLS-DA were 0.546 and 0.0178. The AUC of the best training set and test set models were 0.9726 and 0.9100, 1.0000 and 0.8581, 0.8462 and 0.6263, 1.0000 and 0.7180 evaluated by LASSO, LR, SVM and NN model respectively. The accuracy of the LASSO, LR, SVM and NN model were 87%, 79%, 62%, and 68% in test set, respectively.
UNASSIGNED: The LASSO prediction model of K. pneumoniae sensitivity to imipenem established in this study has a high accuracy rate and has potential clinical decision support ability.
摘要：
■机器学习是人工智能的重要分支和支撑技术，我们建立了4种基于基质辅助激光解吸电离飞行时间质谱(MALDI-TOF-MS)的肺炎克雷伯菌对亚胺培南药物敏感性的机器学习模型,并比较了它们的诊断效果.
■收集2019年1月至2020年12月天津市海河医院微生物科实验室从临床标本中分离的174例肺炎克雷伯菌MALDI-TOF-MS和亚胺培南敏感性数据。随机选择70例亚胺培南敏感和70例耐药病例的质谱和亚胺培南敏感性建立训练集模型,随机抽取17例敏感病例和17例耐药病例建立测试集模型。质谱峰数据进行正交偏最小二乘判别分析(OPLS-DA),通过机器学习最小绝对收缩和选择算子(LASSO)算法建立训练集数据模型,逻辑回归(LR)算法，支持向量机(SVM)算法,神经网络(NN)算法,通过网格搜索和3折交叉验证分别计算和选择训练集和测试集模型的曲线下面积(AUC)和混淆矩阵,通过测试集混淆矩阵验证了预测模型的准确性。
■OPLS-DA的R²Y和Q²分别为0.546和0.0178。LASSO评估的最佳训练集和测试集模型的AUC分别为0.9726和0.9100，1.0000和0.8581，0.8462和0.6263，1.0000和0.7180，LR,分别建立SVM和NN模型。LASSO的准确性,LR,SVM和NN模型分别为87%，79%,62%,和68%的测试集，分别。
■本研究建立的肺炎克雷伯菌对亚胺培南敏感性的LASSO预测模型准确率高，具有潜在的临床决策支持能力。
■收集2019年1月至2020年12月天津市海河医院微生物科实验室从临床标本中分离的174例肺炎克雷伯菌MALDI-TOF-MS和亚胺培南敏感性数据。随机选择70例亚胺培南敏感和70例耐药病例的质谱和亚胺培南敏感性建立训练集模型,随机抽取17例敏感病例和17例耐药病例建立测试集模型。质谱峰数据进行正交偏最小二乘判别分析(OPLS-DA),通过机器学习最小绝对收缩和选择算子(LASSO)算法建立训练集数据模型,逻辑回归(LR)算法，支持向量机(SVM)算法,神经网络(NN)算法,通过网格搜索和3折交叉验证分别计算和选择训练集和测试集模型的曲线下面积(AUC)和混淆矩阵,通过测试集混淆矩阵验证了预测模型的准确性。
■OPLS-DA的R²Y和Q²分别为0.546和0.0178。LASSO评估的最佳训练集和测试集模型的AUC分别为0.9726和0.9100，1.0000和0.8581，0.8462和0.6263，1.0000和0.7180，LR,分别建立SVM和NN模型。LASSO的准确性,LR,SVM和NN模型分别为87%，79%,62%,和68%的测试集，分别。
■本研究建立的肺炎克雷伯菌对亚胺培南敏感性的LASSO预测模型准确率高，具有潜在的临床决策支持能力。
